For more detailed evaluation, we experimented with 17 Outlier Detection DataSets (ODDS) from different domains (collected from odds lab, stonybrook universityhttp://odds.cs.stonybrook.edu). These datasets have one record per data point, and each record contains several attributes. As these datasets have a low number of samples, they are not suitable for neural network and GAN-based approaches. We presented other ten different OCC techniques from [1]} for comparison with NSA techniques. Before applying NSA, we did t-distributed stochastic neighbourhood embedding (t-SNE [2]}) to reduce the dimensionality. We used t-SNE as it provided better visualization and reductions than PCA analysis as PCA works by rotating the vectors for preserving variance, which is suitable for linear, but t-SNE works by minimizing the distance between the point in a Gaussian, and it is a non-linear technique. Unlike PCA, it doesn't get influenced by outliers as it prioritizes local structure instead of the global structure. We applied V-detector methods to all datasets two-dimensional representation space, which acquire using the t-SNE method. We experimented ten times with different test-train split and provided the average of the experimented result.
